Method and system for vehicle-to-pedestrian collision avoidance

ABSTRACT

A method and a system for vehicle-to-pedestrian collision avoidance system, the system comprising participants consisting of Long-Term Evolution (LTE)-capable user equipment (UE) terminals physically linked to at least one vehicle and at least one pedestrian; wherein a spatiotemporal positioning of the terminals is determined from Long Term Evolution (LTE) cellular radio signals mediated by Long-Term Evolution (LTE) cellular base stations (BS) and a Location Service Client (LCS) server including an embedded Artificial Intelligence algorithm comprising a Recurrent Neural Network (RNN) algorithm and analyzes the spatiotemporal positioning of the terminals and determines the likely future trajectory and communicates the likely future trajectory of the participants to the terminals physically linked to the pedestrian; the terminals physically linked to the pedestrian include an embedded Artificial Intelligence algorithm comprising a Conditional Random Fields (CRFs) algorithm to determine if the likely future trajectory of the pedestrian is below a vehicle-to-pedestrian proximity threshold limit and, if this condition is reached, communicates a collision-avoidance emergency signal to the at least one pedestrian and/or vehicle that meet the proximity threshold limit.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/476,461 filed on Jul. 8, 2019, which is a national phase applicationwhich claims benefit of PCT Application No. CA2019/050613 filed on May9, 2019, which claims benefit of U.S. Provisional Application Ser. No.62/669,437, filed on May 10, 2018 and U.S. Provisional Application Ser.No. 62/792,950, filed on Jan. 16, 2019 all of which are incorporatedherein in their entirety by reference.

FIELD OF THE INVENTION

The present invention relates to the field of road safety. Morespecifically, the present invention relates to a method and a system forcollision avoidance between vehicles and pedestrians.

BACKGROUND OF THE INVENTION

Position-based services, such as emergency call positioning for example,drive the development of localization techniques in wirelesscommunications networks. Global Navigation Satellite System(GNSS)-enabled terminals are capable of determining outdoor positionswithin few meters of accuracy, and a number of applications and servicesin terminals take advantage of such accurate positioning.

In telecommunications, Long-Term Evolution (LTE) is a standard forwireless broadband communication for mobile devices and data terminals,based on Enhanced Data rates for GSM Evolution (GSM/EDGE) and UniversalMobile Telecommunications System (UMTS)/High Speed Packet Access (HSPA)technologies. Positioning support in Long-Term Evolution (LTE) for 4Gcellular networks was introduced in 2008. It enables telecom operatorsto retrieve position information of users for location-based servicesand to meet regulatory emergency call positioning requirements.

There are several various techniques that may be used for determiningthe spatiotemporal position of a LTE-capable user equipment (UE). One ofthe most widely used techniques for positioning is based on relativepositioning across an array of serving LTE base stations of knownpositions. Also, Global Positioning System (GPS) offers locationinformation to an accuracy of 5 meters, but exhibits some urban coveragedrawback, seconds-level measurement latencies, and high batteryelectrical consumption, which may limit the applicability of GPS foraccurate vehicle-to-pedestrian collision avoidance.

In the Cell ID technique, the user equipment (UE) can be located usingits serving cell coordinates; the coordinates can either be the basestation (BS) coordinates or sector of a base station (BS) within alocation area Code (LAC). The accuracy of this method relies on theserving cell radius, which however can be as large as 60 km in ruralareas thus providing non-accurate results depending on the userequipment (UE) terminal position.

Several LTE-based techniques may be used to determine the exactspatiotemporal position of a LTE-capable user equipment (UE). In thereceived signal level (RSSI) technique, a user equipment (UE) measuresthe serving and the neighboring cell received signal intensities. Thisinformation can be used to calculate the distance between the userequipment (UE) and the neighboring base station (BS) of known position.If the signals from at least three base stations (BS) are received,triangulation can be applied to the RSSI technique to determine theexact position of the user equipment (UE), since the positions of thebase stations (BS) are known to a high level of accuracy. As usedherein, the term ‘triangulation’ is intended to refer to the geometricaltracing and measurement of a series of triangles in order to determinethe distances and relative positions of points spread over a regioncomprising an array of base stations (BS), by measuring the relativelengths of each sides or by measuring the relative angles of eachcorners, of a triangular baseline. In the time difference of arrival(TDOA) technique, the time difference between each pair of receivedsignals can be estimated by a receiver and the position from theintersection of the two hyperbolas can be determined. In general, thetime difference of arrival (TDOA) measurement is made by measuring thedifference in received phase at each signal in the antenna array. If thesignals from at least three base stations (BS) are received,triangulation can be applied to the TDOA technique to determine theexact position of the user equipment (UE). The angle of arrival (AOA)method includes measuring the angle of arrival of a signal either from abase station (BS) or a user equipment (UE) using for example the antennaemissive patterns. In the angle of arrival (AOA) method, the delay ofarrival at each element in the antenna array is measured directly andconverted to an angle of arrival measurement. Furthermore, thistechnique is a method for determining the direction of propagation of aradio frequency wave incident on the transmitting aerial array. If thesignals from at least three base stations (BS) are received,triangulation can be applied to the angle of arrival (AOA) technique todetermine the exact position of the user equipment (UE). The time ofarrival (TOA) technique uses the speed of light, the speed of radio wavepropagation and the time of a signal arrival to calculate the distanceto determine the actual user equipment (UE) position. If the signalsfrom at least three base stations (BS) are received, triangulation canbe applied to the time of arrival (TOA) technique to determine the exactposition of the user equipment (UE).

In currently deployed Long-Term Evolution (LTE) networks, the UserEquipment (UE) position is usually determined based on a combination ofenhanced cell identity (E-CID), Observed Time Difference of Arrival(OTDOA) and Assisted Global Navigation Satellite Systems (GNSS) (A-GNSS)information from the User Equipment (UE). The level of positioningaccuracy is on the order of tens of meters.

Observed Time Difference of Arrival (OTDOA) is a User Equipment(UE)-assisted method, in which the User Equipment (UE) measures the timeof arrival (TOA) of specific Positioning Reference Signals (PRS)transmitted by cellular Base Stations (BS) and reports the measured Timeof Arrival (TOA) estimates to the location server. The location serverdetermines position of the User Equipment (UE) using multilaterationbased on the Time of Arrival (TOA) measurements of the PositioningReference Signals (PRS) received from at least three base stations andknown location of these base stations.

The positioning accuracy in Observed Time difference of Arrival (OTDOA)method depends on various factors, such as for example networkdeployment, signal propagation condition, synchronization errors, andproperties of Positioning Reference Signals (PRS). For 4G cellular (LTE)indoor users, positioning accuracy can be around 50 meters. For upcoming5G systems, positioning requirements are much stringent, about 1 meteraccuracy for both indoor and outdoor users which include humans,devices, machines, vehicles, etc. For a given deployment and propagationscenario, significant improvements in positioning accuracy is achievableby appropriately redesigning positioning reference signals for 5G radioaccess technology, termed as New Radio (NR).

5G NR (New Radio) is a new radio access technology (RAT) developed by3GPP for the 5G (fifth generation) mobile network. It is meant to be theglobal standard for the air interface of 5G telecommunications networks.The 3rd Generation Partnership Project (3GPP) is a standardsorganization which develops protocols for mobile telephony. The 3GPPspecification 38 series provides the technical details behind NR, theradio access technology (RAT) beyond LTE.

Precise and reliable localization is a topic of high interest forautonomous and unmanned vehicles, such as self-driving cars and dronesfor instance. Indeed, the automotive industry requires positioningaccuracies at the cm-level, in order to enable vehicular use cases basedon automated driving and road safety. Current localization technologiesused for these critical applications are based on Global NavigationSatellite Systems (GNSS) for absolute positioning, and the combinationof radars, cameras and inertial sensors for relative positioning.Nonetheless, the high implementation cost of these on-board sensors mayprevent their adoption in certain applications. Thus, wireless networksdedicated to vehicular-to-everything (V2X) communications can also beexploited for positioning purposes. This is the case of fifth generation(5G) cellular networks, whose disruptive technologies are expected toenable high-accuracy localization.

Vehicle-to-everything (V2X) communication is the passing of informationfrom a vehicle to any entity that may affect the vehicle, and viceversa. It is a vehicular communication system that incorporates othermore specific types of communication as V2I (vehicle-to-infrastructure),V2N (vehicle-to-network), V2V (vehicle-to-vehicle), V2P(vehicle-to-pedestrian), V2D (vehicle-to-device) and V2G(vehicle-to-grid). The main motivations for V2X are road safety, trafficefficiency, and energy savings. There are two types of V2X communicationtechnology depending on the underlying technology being used:WLAN-based, and cellular-based. Standardization of WLAN-basedVehicle-to-everything (V2X) systems supersedes that of cellular-basedVehicle-to-everything (V2X) systems. IEEE first published thespecification of WLAN-based V2X (IEEE 802.11p) in 2012. It supportsdirect communication between vehicles (V2V) and between vehicles andinfrastructure (V2I). In 2016, 3GPP published Vehicle-to-everythingcommunication (V2X) specifications based on Long-Term Evolution (LTE) asthe underlying technology. It is generally referred to as “cellular V2X”(C-V2X) to differentiate itself from the 802.11p based V2X technology.In addition to the direct communication (V2V, V2I), C-V2X also supportswide area communication over a cellular network (V2N). This additionalmode of communication and native migration path to 5G are two mainadvantages over 802.11p based V2X system.

The combination of Global Navigation Satellite Systems (GNSS) andcellular networks has attracted special attention along the differentnetwork generations. Cellular systems are typically considered tocomplement the lack of Global Navigation Satellite Systems (GNSS)visibility in urban environments. Most of these hybrid GNSS/LTEnavigation solutions are necessary to filter noisy GNSS or LTE cellularposition measurements over time. Furthermore, the cellular propagationchannels are dominated by non-line-of-sight (NLoS) conditions and densemultipath. The position errors are above 50 m with 20-MHz Long TermEvolution (LTE) signals from field measurements. Therefore, the 5G newradio (NR) features, such as wideband signals, massive antenna arrays,millimeter wave (mm Wave) transmissions, ultra-dense networks anddevice-to-device (D2D) communications, are expected to significantlyenhance the hybrid positioning performance with Global NavigationSatellite Systems (GNSS). These NR features introduces high-accuracyranging and angle measurements with a high network density, which areenvisaged to achieve high-accuracy positioning. In addition, the 5Gcentimeter-wave (cave) transmissions with extended bandwidths are alsoof interest in macro-cell deployments. In this sense, the 3GPP standardhas just approved a new study item on 5G NR positioning, however, thereis a limited literature on the integration of Global NavigationSatellite Systems (GNSS) and 5G technologies.

The supported positioning methods in Long-Term Evolution (LTE) rely onthe high-level network architecture shown in FIG. 1. There are threemain elements involved in the process, the Location Service Client(LCS), the LCS Server (LS) and the LCS Target. A client, i. e. therequesting service, is in the majority of the cases installed oravailable on the LCS target. The client obtains the location informationby sending a request to the server. The location server is a physical orlogical entity that collects measurements and other location informationfrom the device and base station and assists the device withmeasurements and estimating its position. The server basically processesthe request from the client and provides the client with the requestedinformation and optionally with velocity information. There are twodifferent possibilities for how the device (client) can communicate withthe location server. There is the option to do this over the user plane(U-Plane), using a standard data connection, or over the control plane(C-Plane). In the control plane the E-SMLC (Evolved Serving MobileLocation Center) is of relevance as location server, whereas for theuser plane this is handled by the SUPL Location Platform.

5G communications networks are expected to provide huge improvements inthe capacity, number of connected devices, energy efficiency, andlatencies when compared to existing communications systems. Thesefeatures will be enabled by the combination of higher bandwidths,advanced antenna technologies, and flexible radio access solutions,among others. Especially in urban environments, 5G networks are alsoexpected to consist of densely distributed access nodes (ANs).Consequently, a single user equipment (UE) in such dense networks iswithin coverage range to multiple closely located access nodes (ANs) ata time. Such short user equipment (UE)-access nodes (ANs) distancesprovide obvious benefits for communications, e.g., due to lowerpropagation losses and shorter propagation times, but interestingly canalso enable highly accurate user equipment (UE) positioning. Altogether,5G networks allow for many opportunities regarding acquisition andexploitation of UE location information.

One of the improvements in 5G networks concerns the positioningaccuracy. It is stated that 5G should provide a positioning accuracy inthe order of one meter or even below. That is significantly better thanthe accuracy of a couple of tens of meters provided in long termevolution (LTE) systems by observed time difference of Observed TimeDifference of Arrival (OTDOA) techniques. The required positioningaccuracy in 5G networks will outperform also commercial globalnavigation satellite systems (GNSSs) where the accuracy is around 5 m,and wireless local area network (WLAN) fingerprinting resulting in a 3m-4 m accuracy, Another improvement that 5G networks may provideconcerns the energy efficiency of positioning. This stems from thecommon assumption that 5G networks will exploit frequently transmitteduplink (UL) pilot signals for channel estimation purposes at the ANs.These signals can be used also for positioning in a network centricmanner where the UE location is estimated either independently in theANs or in a centralized fusion center, assuming known AN locations, andthus no calculations are needed in the mobile UEs. Note that this is aconsiderable difference to the device-centric positioning, e.g., GlobalNavigation Satellite Systems (GNSS), where the mobile UEs are underheavy computational burden. Therefore, network-centric positioningtechniques provide significant power consumption improvements and enableubiquitous high-accuracy positioning that can run in the backgroundcontinuously. Such a functionality decreases also the signaling overheadwhen the location information is to be used on the network side, but onthe other hand, requires additional care for privacy as the positioningis not carried out at the UEs themselves. As a third improvement in5G-based positioning, regardless whether it is network- ordevice-centric, location information can be obtained in completeindependence of UE satellite connections everywhere under the networkcoverage area, including also challenging indoor environments.

Positioning information is a central element towards self-drivingvehicles, intelligent traffic systems (ITSs), drones as well as otherkinds of autonomous vehicles and robots. Location-awareness can beexploited also in the UEs as well as by third parties for providingother than purely communications type of services. Taking traffic andcars as an example, up-to-date location information and predicted UEtrajectories can provide remarkable improvements, e.g., in terms oftraffic flow, safety and energy efficiency. When comprehensivelygathered car location information is shared with ITSs, functionalitiessuch as traffic monitoring and control can be enhanced. Accuratelocation information is needed also in the cars themselves, e.g., fornavigation purposes, especially when considering autonomous andself-driving cars. Location-awareness is required also for collisionavoidance. Within communications range cars can report their locationdirectly to other cars, but when the link between the cars is blocked,location notifications are transmitted in collaboration with ITSs.Naturally, the demands and functionalities regarding self-driving carscannot be met everywhere and at all times by existing communicationssystems and satellite based positioning. Consequently, advancedcommunications capabilities and network-based positioning in 5G islikely to play an important role in the development of self-driving carsystems.

The ever-increasing tendency of developing mobile applications foreveryday use has ultimately entered the automotive sector. Vehicleconnectivity with mobile apps have the great potential to offer a betterand safer driving experience, by providing information regarding thesurrounding vehicles and infrastructure and making the interactionbetween the car and its driver much simpler. The fact that apps maysignificantly improve driving safety has attracted the attention of carusers and caused a rise in the number of new apps developed specificallyfor the car industry. This trend has such a great influence that nowmanufacturers are beginning to design cars taking care of theirinteraction with mobile phones.

Vehicle-to-pedestrian collision avoidance methods and systems requireprecise spatiotemporal positioning accuracies of the order of 1 meter orless, in order to discriminate for example a pedestrian crossing thestreet from a pedestrian walking on the sidewalk where significant V2Pcollision probability differences exist. In currently deployed Long-TermEvolution (LTE) networks, the level of spatiotemporal positioningaccuracy is on the order of tens of meters, which may not provide enoughpositioning discrimination and therefore may limit the applicability ofcurrently deployed Long-Term Evolution (LTE) networks for accuratevehicle-to-pedestrian collision avoidance. In currently deployed GlobalPositioning System (GPS), the level of spatiotemporal positioningaccuracy is on the order of 5 meters, but exhibits some urban coveragedrawback, seconds-level measurement latencies, and high batteryelectrical consumption, which may not provide enough spatiotemporalpositioning discrimination and therefore may limit the applicability ofGPS for accurate vehicle-to-pedestrian collision avoidance. Therefore,there is still a need for a method and system for vehicle-to-pedestriancollision avoidance, where upcoming 5G-LTE communications networks andNew Radio (NR) technologies may provide for accuratevehicle-to-pedestrian collision avoidance.

There is still a need for a method and system for vehicle-to-pedestriancollision avoidance.

SUMMARY OF THE INVENTION

More specifically, in accordance with the present invention, there isprovided a method for vehicle-to-pedestrian collision avoidance,comprising physically linking at least one vehicle to at least oneLong-Term Evolution (LTE)-capable user equipment (UE) terminal;physically linking at least one pedestrian to at least one Long-TermEvolution (LTE)-capable user equipment (UE) terminal; determining aspatiotemporal positioning of each terminal determined from Long TermEvolution (LTE) cellular radio signals mediated by at least threeLong-Term Evolution (LTE) cellular base stations (BS) and at least oneLocation Service Client (LCS) server; wherein the at least one LocationService Client (LCS) server includes an embedded artificial intelligencealgorithm comprising a Recurrent Neural Network (RNN) algorithm toanalyze the spatiotemporal positioning of the terminals and determine alikely future trajectory of the at least one vehicle and the at leastone pedestrian so as to maximize a reward metric based on ReinforcementLearning (RL) analysis; and the at least one Location Service Client(LCS) server communicates the likely future trajectory of the at leastone vehicle and the at least one pedestrian to the at least one terminalphysically linked to the at least one pedestrian; the at least oneterminal physically linked to the at least one pedestrian including anembedded Artificial Intelligence algorithm comprising a ConditionalRandom Fields (CRFs) algorithm to determine if the likely futuretrajectory of the at least one pedestrian is below avehicle-to-pedestrian proximity threshold limit; and, if the proximitythreshold limit is reached, the terminal physically linked to the atleast one pedestrian communicates a collision-avoidance emergency signalto the at least one pedestrian and to the at least one vehicle that meetthe proximity threshold limit.

There is further provided a vehicle-to-pedestrian collision avoidancesystem, comprising participants consisting of a set of at least twoLong-Term Evolution (LTE)-capable user equipment (UE) terminalsphysically linked to at least one vehicle and at least one pedestrian;wherein a spatiotemporal positioning of the terminals is determined fromLong Term Evolution (LTE) cellular radio signals mediated by at leastthree Long-Term Evolution (LTE) cellular base stations (BS) and at leastone Location Service Client (LCS) server; the at least one LocationService Client (LCS) server includes an embedded Artificial Intelligencealgorithm comprising a Recurrent Neural Network (RNN) algorithm,analyzes the spatiotemporal positioning of the terminals and determinesthe likely future trajectory of the participants so as to maximize areward metric based on Reinforcement Learning (RL) analysis; andcommunicates the likely future trajectory of the participants to theterminals physically linked to the at least one pedestrian; theterminals physically linked to the at least one pedestrian include anembedded Artificial Intelligence algorithm comprising a ConditionalRandom Fields (CRFs) algorithm to determine if the likely futuretrajectory of the at least one pedestrian is below avehicle-to-pedestrian proximity threshold limit and, if this conditionis reached, the terminal physically linked to the at least onepedestrian communicates a collision-avoidance emergency signal to atleast one of: the at least one pedestrian and the at least one vehiclethat meet the proximity threshold limit.

Other objects, advantages and features of the present invention willbecome more apparent upon reading of the following non-restrictivedescription of specific embodiments thereof, given by way of exampleonly with reference to the accompanying drawings

BRIEF DESCRIPTION OF THE DRAWINGS

In the appended drawings:

FIG. 1 is a schematic view of a high-level network architecturesupporting Long-Term Evolution (LTE)-based geolocation as known in theart;

FIG. 2 is a schematic view of a system according to an embodiment of anaspect of the present invention;

FIG. 3 is a schematic view of a 2D map that may be used to classifyspatiotemporal coordinates of pedestrians and vehicles depending on alevel of risk probability of identified spaces according to anembodiment of an aspect of the present invention;

FIG. 4 shows prediction of likely future trajectory or position of aparticipant based on previous spatiotemporal positioning as determinedby Long-Term Evolution (LTE)-based or Global Navigation SatelliteSystems (GLASS)-based techniques or a combination thereof according toan embodiment of an aspect of the present invention;

FIG. 5 illustrates a method for isolating and discarding spatiotemporalcoordinates that are out of norm behaviour, according to an embodimentof an aspect of the present invention;

FIG. 6 is a graphic representation of acceptance or rejection of normaland abnormal or incoherent spatiotemporal coordinates;

FIG. 7 illustrates a case when a UE terminal linked to a pedestrian (F)exhibiting a normal path (N) starts to exhibit incoherent, absent or outof norm Long-Term Evolution (LTE)-determined spatiotemporal coordinates(H) when passing buildings attenuating Long-Term Evolution (LTE)signals, according to an embodiment of an aspect of the invention;

FIG. 8 illustrates a case when UE terminals linked to pedestrians (F, G)and a UE terminal linked to a vehicle (V) exhibiting spatiotemporalcoordinates start to have incoherent, absent or out of normspatiotemporal coordinates (H: incoherent coordinates due to defectivedevice in vehicle V; I: incoherent coordinates due to adversemeteorological conditions that affect the signal; J: incoherentcoordinates due to reflections from building walls) that can beclassified and rejected, accepted or normalized using artificialintelligence, according to an embodiment of an aspect of the invention;

FIG. 9 illustrates a case, according to an embodiment of an aspect ofthe invention, where an embedded artificial intelligence algorithm canbe used to identify a vehicle (D). Identification of the vehicle may bedone by finding patterns in spatiotemporal coordinates differentiatedfrom patterns in spatiotemporal coordinates of a wheelchair (A), apedestrian crossing the street (B) or a bicycle (C);

FIG. 10 illustrates redundancy of a decision process according to anembodiment of an aspect of the present invention;

FIG. 11 illustrates a logo with proprietary bar code, which may be usedfor example to identify a vehicle comprising a Long-Term Evolution(LTE)-capable user equipment (UE) terminal enabled by an embeddedartificial intelligence algorithm for vehicle-to-pedestrian (V2P)collision avoidance, according to an embodiment of an aspect of theinvention;

FIG. 12 is a detail of FIG. 11;

FIG. 13 is a schematic view of User Equipment (UE) terminals physicallylinked to vehicles that may receive geolocation input from other typesof sensors, according to an embodiment of an aspect of the presentinvention;

FIG. 14 is a schematic view of User Equipment (UE) terminals physicallylinked to vehicles that may receive geolocation input from other typesof sensors, according to an embodiment of an aspect of the presentinvention; and

FIG. 15 is a schematic view of User Equipment (UE) terminals physicallylinked to vehicles and/or pedestrians that may receive geolocation inputfrom other types of sensors distributed in the urban environment.

DETAILED DESCRIPTION OF THE INVENTION

A method and a system for vehicle-to-pedestrian (V2P) collisionavoidance, in the field of intelligent transportation technology anddata analytics with an Artificial Intelligence (AI) algorithm embeddedin a User Equipment (UE) terminal aiming at vehicle-to-pedestrian (V2P)collision avoidance, will now be described by the following non-limitingexamples.

A method and a system for vehicle-to-pedestrian (V2P) collisionavoidance according to an embodiment of an aspect of the invention isillustrated in FIG. 2.

Vehicle-to-pedestrian (V2P) collision avoidance involves at least onevehicle (V) and at least one pedestrian (P). Each pedestrian isphysically linked to at least one Long-Term Evolution (LTE)-capable userequipment (UE) terminal. Each vehicle (V) is physically linked to atleast one Long-Term Evolution (LTE)-capable user equipment (UE)terminal. As used herein, the term ‘physically linked’ is intended torefer to a proximal combination, or association, or attachment, orcoupling between a LTE-capable user equipment and a pedestrian, or avehicle. For example, a Long-Term Evolution (LTE)-capable user equipment(UE) terminal may be physically linked to one pedestrian, such as amobile phone, inserted in the pocket of a pedestrian, or may bephysically linked to one vehicle, such as a mobile phone secured on thedash board of a vehicle.

The spatiotemporal positioning of each user equipment (UE) terminal isdetermined from Long Term Evolution (LTE) cellular radio signalsmediated by Long-Term Evolution (LTE) cellular base stations (BS) and aLocation Service Client (LCS) server. Signals from at least threecellular base stations (BS) may be used in order to use a triangulationmethod to determine the exact position of each user equipment (UE)terminal for positioning the exact position of each user equipment (UE)terminal by triangulation for instance.

The Location Service Client (LCS) server includes an embedded ArtificialIntelligence (AI-1) algorithm, comprising a Recurrent Neural Network(RNN) algorithm for example, to analyze the spatiotemporal positioningof the terminals of the pedestrian (P) and the terminals of the vehicle(V) and determine a likely future trajectory of the pedestrian (P) andof the vehicle (V) so as to maximize a reward metric based onReinforcement Learning (RL) analysis. As used herein, the term “rewardmetric” refers to the goal of minimizing the vehicle-to-pedestriancollision probability such that the Artificial Intelligence algorithmdetermines the best scenario for maximizing the vehicle-to-pedestriancollision avoidance probability. The LCS server communicates the likelyfuture trajectory of the participants to the terminals physically linkedto the pedestrian (P); The terminals physically linked to the pedestrian(P) include an embedded Artificial Intelligence (AI-2) algorithmcomprising a Conditional Random Fields (CRFs) algorithm to determine ifthe likely future trajectory of the pedestrian (P) is below avehicle-to-pedestrian (V2P) proximity threshold limit and, if thiscondition is met, the terminals physically linked to the pedestrian (P)communicate a collision-avoidance emergency signal to the pedestrian (F)and to the vehicle (V) that meet the proximity threshold limit.

Similarly, the LCS server communicates the likely future trajectory ofthe participants to the terminals physically linked to the vehicle (V);The terminals physically linked to the vehicle (V) include an embeddedArtificial Intelligence (AI-2) algorithm comprising a Conditional RandomFields (CRFs) algorithm to determine if the likely future trajectory ofthe vehicle (V) is below a vehicle-to-pedestrian (V2P) proximitythreshold limit and, if this condition is met, the terminals physicallylinked to the vehicle (V) communicate a collision-avoidance emergencysignal to the to the pedestrian (P) and to the vehicle (V) that meet theproximity threshold limit.

The vehicle-to-pedestrian (V2P) proximity threshold limit between theparticipants takes into account position, speed, direction and likelyfuture trajectories of the participants in order to determine adimensional safety margin for establishing proper collision avoidancemeasures, and is of at most 10 meters, for example at most 5 meters, forexample at most 1 meter.

If the signals from at least three base stations (BS) are received,triangulation techniques may be applied to the received signal level(RSSI) technique, to the time difference of arrival (TDOA) technique, orto the angle of arrival (AOA) technique, or to a combination thereof, todetermine the exact position of the user equipment (UE) terminal, sincethe positions of the base stations (BS) are known to a high level ofaccuracy. The User Equipment (UE) terminal position may be determined bya combination of enhanced cell identity (E-CID), Assisted GlobalNavigation Satellite Systems (GNSS) information from the UE, receivedsignal level (RSSI) technique, time difference of arrival (TDOA)technique, or angle of arrival (AGA) technique.

The Long Term Evolution (LTE) may use 5G NR new radio access technology(RAT) developed by 3GPP for the 5G (fifth generation) mobile network.

The User Equipment (UE) terminals as described herein may consist of amobile phone, a wearable device, an Internet of Things (IoT) device, orany other Long-Term Evolution (LTE)-capable device connected to thetelecommunications networks, or any combination thereof. The UserEquipment (UE) terminals may comprise an application, a software, afirmware, a hardware or a device in order to store and activate theembedded Artificial Intelligence (AI-2) algorithm.

The Artificial intelligence (AI-2) algorithm embedded within the UserEquipment (UE) terminals may comprise a recurrent neural network (RNN)algorithm, or a Reinforcement learning (RL) algorithm, or a ConditionalRandom Fields (CRFs) algorithm, or a machine learning (ML) algorithm, ora deep learning (DL) algorithm, or any other artificial intelligencealgorithm, or a combination thereof, A recurrent neural network (RNN) isa class of artificial neural network where connections between nodesform a directed graph along a temporal sequence. This allows the neuralnetwork to exhibit temporal dynamic behavior in which the spatiotemporalcoordinates of a participant is denoted by a matrix X=(x,y,z,t).Reinforcement learning (RL) is an area of machine learning concernedwith how participants ought to take actions in an environment so as tomaximize some notion of cumulative reward. Conditional random fields(CRFs) are a class of statistical modeling method often applied inpattern recognition and machine learning and used for structuredprediction.

The Artificial Intelligence (AI-1) algorithm embedded within the LOSserver may comprise a recurrent neural network (RNN) algorithm, or aReinforcement learning (RL) algorithm, or a Conditional Random Fields(CRFs) algorithm, or a machine learning (ML) algorithm, or a deeplearning (DL) algorithm, or any other artificial intelligence algorithm,or a combination thereof.

The Artificial Intelligence algorithms may be used to predict the likelytrajectory of participants based on small spatiotemporal data sets aswell as large spatiotemporal data sets. A spatiotemporal trajectorymodel may be defined as a set of spatiotemporal points X=(x,y,z,t) of aparticipant moving along a trajectory represented by its geolocationcoordinates in space and time (sequential datasets of participant, timeand location). The data sets may also be spatiotemporal geolocation datathat may comprise other types of data not classified as spatiotemporalpoints, such as image data or audio data or other types of data. Inorder to process sequential datasets, neural networks of deep learning(recurrent neural networks, or RNN) algorithms may be used, RNNs havebeen developed mostly to address sequential or time-series problems suchas sensor' stream data sets of various length. Also, Long Short TermMemory (LSTM) algorithms may be used, which mimics the memory to addressthe shortcomings of RNN due the vanishing gradient problems, preventingthe weight (of a given variable input) from changing its value. RNN isan artificial neural network with hidden layer h_(t), referring to arecurrent state and representing a “memory” of the network through time.The RNN algorithm may use its “memory” to process sequences of inputsx_(t). At each time step t, the recurrent state updates itself using theinput variables x_(t) and its recurrent state at the previous time steph_(t-1), in the form: h_(t)=f(x_(t),h_(t-1)). The function f(xt,ht−1) inturn is equal to g(Wψ(x_(t))+Uh_(t-1)+bh), where ψ(xt) is the functionwhich transforms a discrete variable into a continuous representation,while W and U are shared parameters (matrices) of the model through alltime steps that encode how much importance is given to the current datumand to the previous recurrent state. Variable b is a bias, if any.Whereas neural networks of deep learning models require large data setsto learn and predict the trajectory of a participant, conditional RandomFields (RFs) may be used for the same purpose for smaller data sets. RFsmay be better suited for small datasets and may be used in combinationwith RNN. Models with small datasets may use Reinforcement learningalgorithms when trajectory predictions consider only nearestspatiotemporal geolocation data.

The Artificial Intelligence algorithms may be used to predict the likelytrajectory of participants based on expanded spatiotemporal data setsand other type of data sets, which may relate to the trajectory intentof the vehicle or the pedestrian, including spatiotemporal velocity andacceleration data sets that determine spatiotemporal change of position(dx/dt, dy/dt, dz/dt, d²x/dt², d²y/dt², d²z/dt²), spatiotemporalangular, or gyroscopic, data sets that determine spatiotemporalorientation and change of orientation (θ_(x), θ_(y), θ_(z), dθ_(x),dθ_(y)/dt, dθ_(z), d²θ/dt², d²θ_(y)/dt², d²θ_(z)/dt²), or otherspatiotemporal data sets or a combination thereof. A spatiotemporaltrajectory model may be defined as a set of spatiotemporal points X=(x,y, z, t) or a set of expanded spatiotemporal points X=(x, y, z, t,dx/dt, dy/dt, dz/dt, d²x/dt², d²y/dt², d²z/dt², θ_(x), θ_(y), θ_(z),dθ_(x)/dt, dθ_(y)/dt, dθ_(z)/dt, d²θ_(x)/dt², d²θ_(y)/dt², d²θ_(z)/dt²)of a participant moving along a trajectory represented by itsgeolocation, velocity, and gyroscopic coordinates in three-dimensionalspace and time. The RNN algorithm may use its “memory” to processsequences of inputs=(x, y, z, t, dx/dt, dy/dt, dz/dt, d²x/dt², d²y/dt²,d²z/dt², θ_(x), θ_(y), θ_(z), dθ_(x)/dt, dθ_(y)/dt, dθ_(z)/dt,d²θ_(x)/dt², d²θ_(y)/dt², d²θ_(z)/dt²). At each time step t, therecurrent state updates itself using the input variables xt and itsrecurrent state at the previous time step h_(t-1), in the form:h_(t)=f(x_(t),h_(t-1)).

The Artificial Intelligence algorithm embedded in the User Equipment(UE) terminals may be specific to terminals physically linked to avehicle (V), or to terminals physically linked to a pedestrian (P), orto a LCS server of any kind. For example, the User Equipment (UE)terminals physically linked to a vehicle (V) or to a pedestrian (P) maycomprise a computational unit for processing an artificial Intelligencealgorithm, the computational unit being one of: a mobile application, asoftware, a firmware, a hardware, a physical device, and a computingdevice, or a combination thereof. The Artificial Intelligence algorithmmay use different algorithmic codes in order to provide specific resultsfor different User Equipment (UE) terminals, or to provide specificresults for different end users, who may be related to the automobilesector, or to the cell phone sector, or to the telecommunicationssector, or to the transportation sector, or to any other sectors. Endusers may include automobile OEMs, or cell phone applications providers,or mobile telephony providers, or any other end users.

The User Equipment (UE) terminals may be physically linked to vehiclesincluding autonomous vehicles, non-autonomous vehicles, self-drivingvehicles, off-road vehicles, trucks, manufacturing vehicles, industrialvehicles, safety & security vehicles, electric vehicles, low-altitudeairplanes, helicopters, drones (UAVs), boats, or any other types ofautomotive, aerial, or naval vehicles with some proximity to pedestrianssuch as encountered in urban, industry, airport, or naval environments.The User Equipment (UE) terminals physically linked to vehicles maycomprise a computational unit for processing an artificial Intelligencealgorithm, the computational unit being one of: a mobile application, asoftware, a firmware, a hardware, a physical device, and a computingdevice, or a combination thereof, which may be connected to theartificial Intelligence algorithm (AI-2) to determine if the likelyfuture trajectory of the vehicles is below a vehicle-to-pedestrian (V2P)proximity threshold limit and, if this condition is met, to communicatea collision-avoidance emergency signal. The signal may take the form ofa direct actuation on the vehicle, including changing the direction ofthe vehicle (e.g. course correction), or changing the speed of thevehicle (e.g applying brakes), or sending a signal to the pedestrian(e.g. visual or audio signaling), or any other actuation measures bydirect action on the vehicle's controls for collision avoidance. Forexample, the collision-avoidance emergency signal comprises a decisionprocess for enabling at least one of: changing the direction of thevehicle; changing the speed of the vehicle; and sending a signal to theat least one pedestrian.

The User Equipment (UE) terminals physically linked to vehicles mayreceive geolocation input from other types of sensors including forexample any one of Global Navigation Satellite Systems (GNSS) (or GPS),camera, sonar, lidar, radar, accelerometry, inertial, or gyroscopicsensors, or any other sensors or a combination thereof. The ArtificialIntelligence algorithm (AI-1) may weight or prioritize Long-TermEvolution (LTE) inputs, or GPS inputs, or camera inputs, or sonarinputs, or lidar inputs, or radar inputs, or accelerometry inputs, orgyroscopic inputs depending on the accuracy or reliability of eachinputs. The position of the User Equipment (UE) terminals physicallylinked to vehicles may be determined by other types of sensors embeddedin the terminals including any one of Global Navigation SatelliteSystems (GNSS), camera, sonar, lidar, radar, accelerometry, orgyroscopic sensors, or any other sensors or a combination thereof.

The User Equipment (UE) terminals may be physically linked topedestrians including sidewalk pedestrians, on-road pedestrians,intersection pedestrians, construction workers, manufacturing workers,safety & security workers, airport workers, naval workers, wheelchairusers, bicycle drivers, pets, or any other types of pedestrians. TheUser Equipment (UE) terminals physically linked to pedestrians maycomprise an application, a software, a firmware, a hardware or aphysical or computing device, which may be connected to the artificialIntelligence algorithm (AI-2) to determine if the likely futuretrajectory of the pedestrians is below a vehicle-to-pedestrian (V2P)proximity threshold limit and, if this condition is met, to communicatea collision-avoidance emergency signal. The signal may take the form ofa direct actuation on the vehicle meeting the proximity threshold limit,including changing the direction of the vehicle (e.g. coursecorrection), or changing the speed of the vehicle (e.g. applyingbrakes), or sending a signal to the pedestrian (e.g. visual or audiosignaling), or any other actuation measures by direct action on thevehicle's controls for collision avoidance, or a combination thereof.

The User Equipment (UE) terminals physically linked to pedestrians mayreceive geolocation input from other types of sensors including forexample any one of GPS, camera, sonar, lidar, radar, accelerometry,inertial, or gyroscopic sensors, or any other sensors or a combinationthereof. The Artificial Intelligence algorithm may weight or prioritizeLong-Term Evolution (LTE) inputs, or GPS inputs, or camera inputs, orsonar inputs, or lidar inputs, or radar inputs, or accelerometry inputs,or gyroscopic inputs depending on the accuracy or reliability of eachinputs. The position of the User Equipment (UE) terminals physicallylinked to pedestrians may be determined by other types of sensorsembedded in the terminals including any one of Global NavigationSatellite Systems (GNSS), camera, sonar, lidar, radar, accelerometry, orgyroscopic sensors, or any other sensors or a combination thereof.

FIG. 3 is a schematic of application of a 2D map that may be used toclassify spatiotemporal coordinates of pedestrians (P) and vehicles (V)depending on a level of risk probability of identified spaces. Forexample, spatial coordinates coincident with sidewalks (A) may beclassified as low-probability collision zones between pedestrian andvehicle. Spatial coordinates coincident with streets (B) may beclassified as high-probability collision zones between pedestrian andvehicle. Spatial coordinates coincident with indoor locations (C) may beconsidered as safe zones.

FIG. 4 shows prediction of likely future trajectory or position (30) ofa participant (32) based on previous spatiotemporal positioning asdetermined by Long-Term Evolution (LTE)-based or Global NavigationSatellite Systems (GNSS)-based techniques or a combination thereof (34).Likely future trajectory or position (30) of a participant (32) may bedetermined by an embedded Artificial Intelligence (AI-2) algorithmcomprising a recurrent neural network (RNN) algorithm, or aReinforcement learning (RL) algorithm, or a Conditional Random Fields(CRFs) algorithm, or a machine learning (ML) algorithm, or a deeplearning (DL) algorithm, or any other artificial intelligence algorithm,or a combination thereof. Likely future trajectory or position (30) of aparticipant (32) may be based on an analysis of the participantsprevious spatiotemporal coordinates, its instant position, its speed,and on a level of risk probability of identified spaces. Also, likelyfuture trajectory or position (30) of a participant (32) may be based onan analysis of the participants previous spatiotemporal trajectory modelthat may be defined as a set of spatiotemporal points X=(x, y, z, t) oras a set of expanded spatiotemporal points X=(x, y, z, t, dx/dt, dy/dt,dz/dt, d²x/dt², d²y/dt², d²z/dt², θ_(x), θ_(y), θ_(z), dθ_(x)/dt,dθ_(y)/dt, dθ_(z)/dt, d²θ_(x)/dt², d²θ_(y)/dt², d²θ_(z) ²) of aparticipant moving along a trajectory represented by its geolocation,velocity.

FIG. 5 shows the use of an embedded artificial intelligence algorithmbased on a recurrent neural network (RNN) algorithm, or a Reinforcementlearning (RL) algorithm, or a Conditional Random Fields (CRFs)algorithm, or a machine learning (ML) algorithm, or a deep learning (DL)algorithm, or any other artificial intelligence algorithm, or acombination thereof) to isolate and discard spatiotemporal coordinatesof a pedestrian (F) or a vehicle (E) that are out of norm behaviour (H),where (G) represents past and coherent spatiotemporal coordinates. Outof norm coordinates (H) can be incoherent, absent or too different fromthe position's prediction: they may result from a geographic zoneinferring with the Long-Term Evolution (LTE) device operation such asfor example a space between two buildings where walls attenuatesLong-Term Evolution (LTE) signals or an otherwise defective userequipment (EU).

FIG. 6 is a graphic representation of acceptance or rejection by thenorm (N) of normal (X) and abnormal or incoherent (H) spatiotemporalcoordinates, respectively, and their classification according to aprecision (P) set by the system for a specific pedestrian, device,vehicle, geographic area or at large. The precision (P) is selected inorder to disqualify out of bound results from normal movement. The norm(N) can follow many possible mathematic distributions and selectedaccording to specific UE, specific pedestrian, specific Long-TermEvolution (LTE) device, specific vehicle or geographic area.

FIG. 7 illustrates a case where a UE terminal linked to a pedestrian (F)exhibiting a normal path (N) starts to exhibit incoherent, absent or outof norm Long-Term Evolution (LTE)-determined spatiotemporal coordinates(H) when passing buildings attenuating Long-Term Evolution (LTE) signalsfor instance. These spatiotemporal coordinates (H) may be rejected,accepted or normalized using artificial intelligence based on geographiczone, weather, location signal, device, vehicle or user. For example,suddenly random spatiotemporal coordinates may be immediately rejected,whereas a linear progression of spatiotemporal coordinates coincidentwith a sidewalk or some other safe area may be accepted. A linearprogression of coordinates suspected to be altered by, for instance,weather or surrounding elements may be normalized, i.e. recalculated andthen considered reliable spatiotemporal coordinates.

FIG. 8 illustrates a case where UE terminals linked to pedestrians (F,G) and a UE terminal linked to a vehicle (V) exhibiting spatiotemporalcoordinates start to have incoherent, absent or out of normspatiotemporal coordinates (H: incoherent coordinates due to defectivedevice in vehicle V; I: incoherent coordinates due to adversemeteorological conditions that affect the signal; J: incoherentcoordinates due to reflections from building walls) that can beclassified and rejected, accepted or normalized using artificialintelligence based on geographic zone, weather, location signal, device,vehicle V or user, According to classification decision, an alarm can besent to pedestrian, inboarded person or vehicle V.

FIG. 9 illustrates a case where an embedded artificial intelligencealgorithm based on a recurrent neural network (RNN) algorithm, or aReinforcement learning (RL) algorithm, or a Conditional Random Fields(CRFs) algorithm, or a machine learning (ML) algorithm, or a deeplearning (DL) algorithm, or any other artificial intelligence algorithm,or a combination thereof, can be used to identify a vehicle (D).Identification of the vehicle may be done by finding patterns inspatiotemporal coordinates differentiated from patterns inspatiotemporal coordinates of a wheelchair (A), a pedestrian crossingthe street (B) or a bicycle (C) for example. Identification method isbased on patterns in spatiotemporal coordinates within mapping zones,i.e. regions of the environment as discussed hereinabove in relation toFIG. 3 for example, taking into account position, speed and directiondata.

The embedded artificial intelligence algorithm may also be used tomanage signal and battery life of the UE terminals and not to overloadthe Location Service Client (LCS) server, based on mapping zones, i.e.regions of the environment as discussed hereinabove in relation to FIG.3 for example. Since sidewalks represent safe zones for pedestrians, therefreshing rate of collected spatiotemporal coordinates may be set tonormal, for instance 2 spatiotemporal coordinates per second. Incontrast, in the case of streets that represent dangerous zones forpedestrians as well as for bicycles and wheelchairs for example, therefreshing rate of collected spatiotemporal coordinates may be set tohigh, such as 4 spatiotemporal coordinates per second for instance. Onthe other hand, as indoor environments such as buildings, may beconsidered safe zones for pedestrians, the refreshing rate of collectedspatiotemporal coordinates may be set to low, for instance 1spatiotemporal coordinate per 30 seconds.

Still referring to FIG. 9, the embedded artificial intelligencealgorithm may also be used to determine the size, area and shape of thevehicle-to-pedestrian (V2P) proximity threshold limit, based on likelyfuture trajectories of the participants and on mapping zones (i.e.regions of the environment as discussed hereinabove in relation to FIG.9 for example). Since sidewalks represent safe zones for pedestrians,the vehicle-to-pedestrian (V2P) proximity threshold limit for asidewalker (PTa) may be set to the size of the sidewalk itself (usuallyless than 3 meters). Whereas, as streets represent dangerous zones forpedestrians as well as bicycles and wheelchairs, thevehicle-to-pedestrian (V2P) proximity threshold limit for a wheelchairriding the side of the street (PTc) may be set to a larger size (3 to 5meters) and the vehicle-to-pedestrian (V2P) proximity threshold limitfor a pedestrian crossing the middle of the street (PTb) may be set toan even larger size (above 5 meters) taking into account position,speed, direction and likely future trajectories of the participants inorder to determine a dimensional safety margin for establishing propercollision avoidance measures.

The UE terminals comprise an embedded artificial intelligence algorithm(based on a recurrent neural network (RNN) algorithm, or a Reinforcementlearning (RL) algorithm, or a Conditional Random Fields (CRFs)algorithm, or a machine learning (ML) algorithm, or a deep learning (DL)algorithm, or any other artificial intelligence algorithm, or acombination thereof) that is used to determine if the likely futuretrajectory of the participants is below a vehicle-to-pedestrian (V2P)proximity threshold limit and, if this condition is met, the terminalsphysically linked to the pedestrians (P) communicate acollision-avoidance emergency signal to the pedestrians (P) and tovehicles (V) that meet the proximity threshold limit. Thecollision-avoidance emergency signal may take the form of an audiosignal, or a visual signal, or a haptic signal, or a radio signal, orany signal, or a combination thereof, adapted to the sensing ability ofthe pedestrians and the actuation ability of the pedestrians' UEterminals. The collision-avoidance emergency signal may also include aradio signal adapted to the actuation ability of the UE terminals linkedto the vehicles meeting the proximity threshold limit. Other collisionavoidance measures may also be considered.

According to an embodiment of an aspect of the present invention, theartificial intelligence algorithm embedded within the Long-TermEvolution (LTE)-capable user equipment (UE) terminals may is used for adecision process if the proximity threshold limit is reached. Thedecision process may be distributed over a plurality of UE terminals andover the network in order to provide redundancy for thecollision-avoidance measures, as well as enhanced reliability andsafety.

FIG. 10 illustrates redundancy of the decision process according to oneembodiment of the present invention. The decision process of the UEterminal physically linked to the vehicle may include measures forslowing down the vehicle, or for applying brakes, or for changingdirection, taking into account position, speed, direction and likelyfuture trajectory of other participants. The decision process of the UEterminal physically linked to the pedestrian may include measures forslowing down, moving away, or changing course, taking into accountposition, speed, direction and likely future trajectory of otherparticipants. The decision process may take place within the centralnetwork and platform (A), and/or on the pedestrian's device (C), and/orin the vehicle's device (B), and/or within the vehicle's neural networkmodule (F), and/or by fog computing (D). Other decision processes mayalso be considered.

FIGS. 11 and 12 illustrate a logo with proprietary bar code, which maybe used for example to identify a vehicle comprising a Long-TermEvolution (LTE)-capable user equipment (UE) terminal enabled by anembedded artificial intelligence algorithm (based on a recurrent neuralnetwork (RNN) algorithm, or a Reinforcement learning (RL) algorithm, ora Conditional Random Fields (CRFs) algorithm, or a machine learning (ML)algorithm, or a deep learning (DL) algorithm, or any other artificialintelligence algorithm, or a combination thereof) forvehicle-to-pedestrian (V2P) collision avoidance. A scan of the logo mayprovide inspection information or any other information related to thevehicle's brand, model and color in order to authenticate theintegrality of the installed AI devices. The logo may be personalizedaccording to the vehicle color patterns, band, model or other featuresand may include other authentication technologies to certify the date ofinstallation and inspection.

Still referring to FIGS. 11 and 12, the logo with proprietary bar codemay be used also to identify a pedestrian wearing a Long-Term Evolution(LTE)-capable user equipment (UE) terminal enabled by an embeddedartificial intelligence algorithm (based on a recurrent neural network(RNN) algorithm, or a Reinforcement learning (RL) algorithm, or aConditional Random Fields (CRFs) algorithm, or a machine learning (ML)algorithm, or a deep learning (DL) algorithm, or any other artificialintelligence algorithm, or a combination thereof) forvehicle-to-pedestrian (V2P) collision avoidance. The logo may consist ofa tag or a label integrated to the pedestrian user equipment (UE)terminal, or to the pedestrian clothes, or to a wearable piece oftextile, or to a textile apparel. A scan of the logo may provideidentity information related to the pedestrian in order to authenticatethe integrality of the AI devices. The logo may be personalizedaccording to the terminal or to the clothing color patterns, band, modelor other features and may include other authentication technologies tocertify the date of installation and inspection.

FIGS. 13 and 14 shows User Equipment (UE) terminals physically linked tovehicles that may receive geolocation input from other types of sensors,according to an embodiment of an aspect of the present invention. FIG.15 shows User Equipment (UE) terminals physically linked to vehiclesand/or pedestrians that may receive geolocation input from other typesof sensors distributed in the urban environment.

There is thus provided a method and a system for vehicle-to-pedestrian(V2P) collision avoidance using Artificial Intelligence (AI) algorithmsembedded in User Equipment (UE) terminals for data analytics, decisionand preventive action taking.

As discussed hereinabove in relation for example with FIG. 2, dataanalytics is performed using Artificial Intelligence (AI) algorithmembedded in User Equipment (UE) terminals, Participants consist of a setof at least two Long-Term Evolution (LTE)-capable user equipment (UE)terminals physically linked to at least one vehicle (V) and at least onepedestrian (P). The spatiotemporal positioning of the terminals isdetermined from Long Term Evolution (LTE) cellular radio signalsmediated by at least three Long-Term Evolution (LTE) cellular basestations (BS) and at least one Location Service Client (LCS) server,which includes an embedded Artificial Intelligence (AI-1) algorithm toanalyze the spatiotemporal positioning of the terminals and determinethe likely future trajectory of the participants. The LCS servercommunicates the likely future trajectory of the participants to theterminals physically linked to the participants. The terminalsphysically linked to the at least one pedestrian (P) include an embeddedArtificial Intelligence (AI-2) algorithm to determine if the likelyfuture trajectory of the at least one pedestrian (P) is below avehicle-to-pedestrian (V2P) proximity threshold limit, and, if thiscondition is met, the terminals physically linked to the at least onepedestrian (P) communicate a collision-avoidance emergency signal to theat least one pedestrian (P) and to the at least one vehicle (V) thatmeet the proximity threshold limit.

As discussed hereinabove in relation for example to FIGS. 2 and 11, theLCS server communicates the likely future trajectory of the participantsto the terminals physically linked to the at least one vehicle (V). Theterminals physically linked to the at least one vehicle (10) include anembedded Artificial Intelligence (AI-2) algorithm to determine if thelikely future trajectory of the at least one vehicle (10) is below avehicle-to-pedestrian (V2P) proximity threshold limit and, if thiscondition is met, the terminal physically linked to the at least onevehicle (10) communicates a collision-avoidance emergency signal to theat least one pedestrian (P) that meets the proximity threshold limit.

As illustrated for example in FIGS. 11, 13 and 14 for example, the UserEquipment (UE) terminals physically linked to vehicles may receivegeolocation input from Long-Term Evolution (LTE) geolocation inputs, andfrom other types of sensors including for example any one of GlobalNavigation Satellite Systems (GNSS) (or GPS), camera, sonar, lidar, orradar sensors, or any other sensors or a combination thereof. TheArtificial Intelligence algorithm (AI-2) may weight or prioritizeLong-Term Evolution (LTE) inputs, or GPS inputs, or camera inputs, orsonar inputs, or lidar inputs, or radar inputs according to the accuracyor reliability of each inputs, to the spatiotemporal position of theparticipants, to the road conditions, or any other data of interest.

As illustrated for example in FIG. 15 for example, the User Equipment(UE) terminals physically linked to vehicles and/or pedestrians mayreceive geolocation input from other types of sensors distributed in theurban environment, including for example any one of Global NavigationSatellite Systems (GNSS) (or GPS), camera, sonar, lidar, or radarsensors, or any other sensors or a combination thereof distributed inthe urban environment. The sensors distributed in the urban environmentmay consist of Long-Term Evolution (LTE) micro-base stations, orLong-Term Evolution (LTE) femto-base stations, or sensors integrated tocity lights, or sensors integrated to streetlights, or sensorsintegrated to traffic monitoring devices, or any other sensors orcombination thereof. The sensors distributed in the urban environmentmay exhibit narrow or wide sensing coverages, and the sensing coveragesmay cover one or several streets. The Artificial intelligence algorithmmay weight or prioritize Long-Term Evolution (LTE) inputs, or GPSinputs, or camera inputs, or sonar inputs, or lidar inputs, or radarinputs according to the accuracy or reliability of each sensor inputs,to the spatiotemporal position of the participants, to the roadconditions, to the weather conditions, or any other data of interest.The position of the User Equipment (UE) terminals physically linked tovehicles and/or pedestrians may be determined by other types of sensorsembedded in the terminals including any one of Global NavigationSatellite Systems (GLASS), camera, sonar, lidar, or radar sensors, orany other sensors or a combination thereof, and may be assisted with ageolocation input from other types of sensors distributed in the urbanenvironment.

The scope of the claims should not be limited by the embodiments setforth in the examples but should be given the broadest interpretationconsistent with the description.

The invention claimed is:
 1. A vehicle-to-pedestrian collision avoidancesystem, comprising participants consisting of a set of at least twoLong-Term Evolution (LTE)-capable user equipment (UE) terminalsphysically linked to at least one vehicle and at least one pedestrian;wherein a spatiotemporal positioning of the terminals is determined fromLong Term Evolution (LTE) cellular radio signals mediated by at leastthree Long-Term Evolution (LTE) cellular base stations (BS) and at leastone Location Service Client (LCS) server; the at least one LocationService Client (LCS) server includes an embedded Artificial Intelligencealgorithm comprising a Recurrent Neural Network (RNN) algorithm,analyzes the spatiotemporal positioning of the terminals and determinesthe likely future trajectory of the participants so as to maximize areward metric based on Reinforcement Learning (RL) analysis; andcommunicates the likely future trajectory of the participants to theterminals physically linked to the at least one pedestrian; theterminals physically linked to the at least one pedestrian include anembedded Artificial Intelligence algorithm comprising a ConditionalRandom Fields (CRFs) algorithm to determine if the likely futuretrajectory of the at least one pedestrian is below avehicle-to-pedestrian proximity threshold limit and, if this conditionis reached, the terminal physically linked to the at least onepedestrian communicates a collision-avoidance emergency signal to atleast one of: the at least one pedestrian and the at least one vehiclethat meet the proximity threshold limit.
 2. The system of claim 1,wherein the terminals physically linked to the at least one vehicleinclude an embedded Artificial Intelligence algorithm comprising aConditional Random Fields (CRFs) algorithm to determine if the likelyfuture trajectory of the at least one vehicle is below thevehicle-to-pedestrian proximity threshold limit and, if this conditionis reached, the terminal physically linked to the at least one vehiclecommunicates the collision-avoidance emergency signal to the at leastone pedestrian that meets the proximity threshold limit.
 3. The systemof claim 1, wherein the Long Term Evolution (LTE) uses 5G NR new radioaccess technology (RAT) developed by 3GPP for the 5G (fifth generation)mobile network.
 4. The system of claim 1, wherein the spatiotemporalpositioning of the terminals is determined by sensors embedded in theterminals, said sensors comprising at least one of: Global NavigationSatellite Systems (GNSS, GPS), camera, sonar, lidar, radar,accelerometry, inertial, and gyroscopic sensors.
 5. The system of claim1, wherein the spatiotemporal positioning of the terminals is determinedby sensors embedded in the terminals, said sensors comprising at leastone of: Global Navigation Satellite Systems (GNSS, GPS), camera, sonar,lidar, radar, accelerometry, inertial, and gyroscopic sensors andwherein the spatiotemporal positioning of the terminals receivesgeolocation input from sensors distributed in the urban environment. 6.The system of claim 1, wherein the User Equipment (UE) terminalscomprise a computational unit for processing an artificial Intelligencealgorithm, the computational unit being at least one of: a mobileapplication, a software, a firmware, a hardware, a physical device, anda computing device.
 7. The system of claim 1, wherein thecollision-avoidance emergency signal comprises a decision process forenabling at least one of: changing the direction of the vehicle;changing the speed of the vehicle; and sending a signal to the at leastone pedestrian.
 8. The system of claim 1, wherein said participantscomprise a logo with proprietary bar code.